Instructions to use JJ-Tae/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JJ-Tae/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JJ-Tae/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JJ-Tae/results") model = AutoModelForMaskedLM.from_pretrained("JJ-Tae/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from JJ-Tae/results: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/JJ-Tae/results/resolve/main/model.safetensors
- Command line
-
hf download hf://JJ-Tae/results/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/JJ-Tae/results/resolve/main/model.safetensors
557 MB
- Xet hash:
- 56b7428bad74fe31f186245e1d8828ad62843651ddf04032a5aea43adc14b981
- Size of remote file:
- 557 MB
- SHA256:
- fbbd510ff7239cd0ed89db3a51a818d5a6c815f7724ad5e506054265f0d4f15e
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